Search Results (628)

To suppress the severe output torque fluctuations caused by clutch engagement when a hybrid electric vehicle equipped with a dedicated hybrid transmission (DHT) switches from pure electric (E) drive mode to hybrid (H) drive mode, a coordinated control method for power source switching is proposed. First, an adaptive fuzzy proportional-integral (PI) controller regulates the engine speed based on the speed difference between the engine and the P2 motor. Second, an active disturbance rejection control (ADRC) controller is employed for trajectory tracking to eliminate the speed difference across the synchronizer’s friction surfaces. This compensates for clutch torque variations during engine startup and ensures rapid synchronizer engagement. Finally, the torque interruption caused by the decoupling of the engine and P2 motor from the driveline is compensated via feedforward control from the P3 motor. The proposed strategy was validated through MATLAB Simulink simulations and CANape calibration tests. The results indicate that applying the proposed method to E-H mode switching slightly extended the total duration by 0.02 s. However, compared with uncoordinated control, the maximum longitudinal jerk was reduced by 73.8%, and the clutch sliding work decreased by 38.6%. This significantly enhances switching smoothness and prolongs the clutch’s service life. Full article

We developed a hardware-in-the-loop simulation of a battery management system (BMS) using controller area network (CAN) as the communication backbone for electric-powered response boats in flood rescue. A LiFePO4 pack and discharge motor/charger were modeled in MATLAB/Simulink/Simscape, while an STM32 Nucleo-F446RE executed CAN messaging. The BMS monitored voltage, current, temperature, and state of charge. Results indicate CAN’s reliability under rescue-like disturbances: priority arbitration delivered over-temperature and over-current warnings ahead of routine telemetry; error detection and retransmission preserved data integrity; and bus-load analysis showed low latency for urgent frames without interrupting state-of-charge reporting, improving situational awareness and reducing operator risk. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Background: Qualitative health research increasingly emphasizes relational and interactional processes in illness and caregiving; however, joint interview formats remain methodologically under-theorized. This article advances a relational and power-sensitive reconceptualization of the couple interview by conceptualizing the interview encounter itself as an interactional site in which caregiving relations become observable in real time rather than merely reported retrospectively. Methods: The article draws on seven in-home couple interviews with long-married older heterosexual couples in Germany, in which one partner provided long-term home-based care for the other. The analysis applies the Documentary Method to reconstruct jointly produced meanings, collective orientations, and the micro-interactional dynamics of the interview situation itself. Results: The analysis shows that couple interviews provide a distinctive methodological lens for studying dyadic caregiving by rendering co-narration, negotiated speaker roles, “we”-positioning, speaking-for-the-other, and embodied coordination analytically visible. Interactional asymmetries, interruptions, and situational role shifts thus emerge not only as challenges but as epistemic resources for reconstructing caregiving relationships and power dynamics. Based on this analysis, the article develops a three-part practice-oriented methodological toolkit comprising relational interviewing strategies, moderation practices, and systematic observation and documentation markers. Conclusions: By reframing the couple interview as an interactional event and specifying analytic markers and conduct strategies, this article makes an explicit methodological contribution to dyadic qualitative health research, particularly in sensitive later-life caregiving contexts. Full article

Recently, distributed DC microgrids have gained prominence due to their modular design, scalability, and seamless integration with renewable energy sources. However, ensuring robust operation of distributed secondary control schemes remains challenging, particularly in the presence of unavoidable communication disruptions and parametric uncertainties encountered in practice. Most existing control strategies either assume ideal communication networks or address fault tolerance and communication constraints separately, which limits their applicability in realistic networked environments. This paper proposes an event-triggered fault-tolerant distributed secondary control framework for DC microgrids operating under communication faults. An embedded averaged model is incorporated to support fault-tolerant decision-making and to guide event-triggered communication updates. In addition, an auxiliary recovery mechanism is introduced, enabling neighboring converters to cooperatively compensate for information loss during communication interruptions without centralized supervision. Lyapunov-based stability analysis establishes boundedness and practical convergence of the closed-loop system under event-triggered updates and bounded disturbances while explicitly excluding Zeno behavior. The simulation results under communication fault scenarios demonstrate that the proposed approach achieves accurate DC bus voltage regulation with steady-state deviations below 1% while restoring proportional power sharing with an averaged error within 5%. The embedded model error remains bounded throughout the fault interval, and fault-tolerant control actions are triggered sparsely with well-separated inter-event times on the order of tens of milliseconds, thereby significantly reducing the communication burden. These results confirm the effectiveness and robustness of the proposed framework for the resilient operation of distributed DC microgrids under practical communication constraints. Full article

The energy transition and electrification are increasing the use of power electronics in low-voltage networks, increasing losses and reducing service availability when harmonic currents are concentrated in the neutral. This study statistically evaluates power quality in a campus-type microgrid with a high proportion of nonlinear loads. The novelty of the work lies in combining field measurements, percentile-based neutral-current severity analysis, and standards-based comparative mitigation assessment in a low-voltage 3P4W campus microgrid. A campaign was carried out using a Fluke 1775 analyzer, recording trends, frequency, and events. Approximately 1900 events were recorded, mainly waveform deviations, interruptions, and rapid voltage changes. Voltage distortion was moderate, with a 95th percentile between 3.6% and 3.8%, while the neutral conductor concentrated the highest severity: neutral-current THD exceeded 220% in the 95th percentile and reached maximums above 700%, with 16.78 A in the 95th percentile at the measurement point. Based on IEC 61000-2-2 and IEEE 519, four mitigation measures were evaluated in DIgSILENT PowerFactory 2024 to estimate and reduce losses and heating: load balancing, detuned compensation, passive filtering, and active filtering. Active mitigation reduced the neutral harmonic component by 80% and the combined strategy decreased the neutral current at the measuring point by 78% (16.78 A to 3.69 A), with an estimated reduction in resistive losses of close to 95%. These results suggest sustainability benefits by reducing energy wasted as heat, extending the useful life of the infrastructure and improving operational resilience. Full article

Detecting and disconnecting faults is of utmost importance in power systems to prevent damage, outages and limit the impact on the surrounding grid. However, there are faults that may not be detected by protective functions and therefore do not interrupt the operation. Such faults, which have not been considered during the design of an HVDC system despite causing negative operational impacts, are referred to as abnormal failure modes in this paper. Data from three cases of abnormal failure modes in point-to-point HVDC systems are presented. The first case regards a prolonged subsequential failure of a DC filter capacitor for an LCC-HVDC link. The second case presents a measurement disturbance resulting in power oscillations from a VSC-HVDC link. The third case shares details of an overload scenario of a grounding impedance due to DC voltage unbalance from asymmetric corona discharges. This study shares details from these failures and suggests recommendations based on the presented abnormal failure modes in HVDC applications, including multi-terminal HVDC systems. Full article

Proton exchange membrane fuel cells (PEMFCs) play a key role in hydrogen-based energy systems; however, accurate and practical modelling remains challenging due to system nonlinearities, parameter variability, and degradation effects. This paper presents a low-complexity parameter estimation methodology for a simplified PEMFC equivalent circuit model using current-switching techniques. The approach enables direct extraction of key parameters, including internal resistance and capacitance, from transient voltage responses without requiring complex optimization or large datasets. Experimental validation was conducted using 100 W and 1 kW PEMFC systems under current loading and interruption conditions. The results demonstrate good agreement between measured and simulated voltage responses, with a maximum error below 10% and typical error levels in the range of ~1.4–3%. Compared to conventional mechanistic and data-driven models, the proposed method significantly reduces computational complexity and measurement requirements while maintaining high predictive accuracy. Moreover, the combination of the simplified equivalent circuit model with current-switching-based parameter estimation offers an effective and practical tool for electrical engineers, enabling real-time monitoring, control-oriented modelling, and seamless integration with power electronic systems. The proposed approach is particularly suitable for applications in DC microgrids and digital twin-based monitoring of hydrogen energy systems. Full article

Background/Objectives: Treatment disruptions and discontinuations during systemic cancer therapy are common and can compromise treatment delivery and outcomes. Structured exercise and mind–body interventions improve cancer-related symptoms, but their impact on treatment disruptions and discontinuations remains unclear. This secondary analysis of the IMPROVE trial evaluated whether participation in Integrative Medicine at Home (IM@Home), a digital multimodal mind–body and structured exercise program, was associated with differences in treatment discontinuation and related treatment disruption outcomes among patients undergoing systemic therapy. Methods: A total of 127 adults with solid tumors were randomized to IM@Home (n = 64) or enhanced usual care (EUC; n = 63) for 12 weeks. Treatment discontinuation, dose delays, dose reductions, and overall treatment disruptions were compared between arms using chi-square tests and regression models adjusted for cancer type and disease stage. Results: In unadjusted analyses, treatment discontinuation occurred less frequently in the IM@Home group compared with EUC (9.4% vs. 22.6%; p = 0.043), but this association was attenuated after adjustment for cancer type and disease stage (aOR 0.41, 95% CI 0.13–1.17; p = 0.105). The proportion of patients experiencing any treatment disruption, as well as rates of dose delays and dose reductions, did not differ significantly between groups (p = 0.16, p = 0.18, and p = 0.85, respectively). In contrast, IM@Home participants experienced fewer treatment disruption events per patient (adjusted RR 0.58, 95% CI 0.35–0.96; p = 0.036). Conclusions: These exploratory findings suggest that digital structured exercise and mind–body programs may help mitigate treatment interruptions during systemic cancer therapy and should be explored further in an adequately powered prospective trial to confirm these promising findings. Full article

Bridge structural health monitoring (BSHM) systems are essential for assessing the operational performance and safety of long-span bridges. However, monitoring data are often affected by factors such as sensor malfunctions, environmental disturbances, or power interruptions, leading to various anomalous data. Moreover, the multiclass imbalance of the data presents a major challenge to traditional anomaly detection methods. To address this issue, a novel multiclass anomaly detection method based on an improved deep convolutional neural network is proposed. Specifically, a ResNet50 architecture integrated with the convolutional block attention module (CBAM) is developed to enhance the extraction of discriminative features. Additionally, the Focal Loss function is introduced to emphasize the loss weight of minority samples, reducing the influence of majority classes, thereby effectively overcoming the class imbalance issue in multiclass anomaly detection. The proposed method is trained and validated using measured acceleration data collected from a large-scale cable-stayed bridge. The experimental results indicate that the model achieves an overall accuracy of 98.28%, while effectively improving the classification performance of minority categories. The method further reproduces the spatiotemporal distribution of anomalies in full-month monitoring data, confirming its robustness and engineering applicability for large-scale automated anomaly diagnosis in BSHM systems. Full article

Earthquakes often cause prolonged electricity outages that disrupt essential health services and basic water, sanitation, and hygiene functions in hospitals and field clinics. This study combines a focused literature review with a time-step energy balance simulation developed in MATLAB 25.2 and Simulink to examine how power interruptions translate into public health risks and to evaluate microgrid-based resilience solution designs. Conventional electricity supply with diesel backup is compared with hybrid solar power, battery storage, and diesel generator configurations under five outage scenarios that vary by duration, fuel availability, and solar conditions. The results indicate that diesel-only strategies are highly vulnerable to fuel supply disruptions, leading to substantial downtime of critical services and increased unmet essential electricity demand. Hybrid microgrid configurations demonstrated a significant improvement in critical-load continuity, thereby enhancing the capacity to sustain essential care during prolonged outages. In the fuel-constrained 72 h outage scenario (S2: 24 h diesel availability), the hospital case shows critical service availability increasing from ~48% (diesel-only) to ~87% (PV + battery + diesel), with similar improvements for the field clinic (~46% to ~85%). Hybrid microgrids improve critical-load continuity via solar generation, battery buffering, and priority-based load shedding, while reducing diesel runtime and extending fuel autonomy. The model also relates energy performance to a WASH-supportability proxy relevant to infection prevention. Full article

On-load tap changers (OLTCs) are critical components of power transformers. In recent years, condition monitoring technologies for OLTCs based on vibration signals have attracted increasing research interest. However, practical applications still face several challenges, including background noise interference, insufficient characterization of transient signals, signal complexity, difficulty in detecting subtle anomalies, and ambiguous associations between fault modes and signal features. To address these issues, this paper proposes an OLTC acoustic fingerprint feature recognition method based on multidimensional phase-space trajectory analysis. First, an OLTC fault simulation platform was established, in which typical mechanical faults—such as fastener loosening, contact wear, and insufficient spring energy storage—were physically simulated. Corresponding vibration signals were then acquired under different operating conditions. Considering the independence of vibration characteristics at different locations of the distribution transformer, a blind source separation method based on endpoint detection was employed to separate OLTC vibration signals from the operational noise of the transformer body. Given the nonlinear and chaotic characteristics of OLTC vibration signals, phase-space reconstruction was introduced for signal analysis. Based on the reconstructed phase space, characteristic patterns and geometric feature parameters corresponding to different mechanical states of the OLTC were extracted. Furthermore, a two-dimensional membership function was constructed using the phase-space trajectories, and fuzzy inference based on predefined fuzzy rules was applied to compute representative feature parameters. A feature parameter database was subsequently established to enable OLTC condition identification. Experimental results demonstrate that the proposed diagnostic model can effectively classify and identify OLTC fault conditions using vibration signals, achieving an average classification accuracy exceeding 91.25%. The proposed method provides an effective non-intrusive approach for online monitoring and mechanical fault diagnosis of OLTCs without interrupting normal transformer operation. Full article

This paper proposes a novel solution to suppress backward wave oscillation (BWO) in high-order mode (HOM) sheet beam (SB) slow-wave structures (SWSs) and designs an isolator between cavities based on a Bragg resonator. This method can cut-off the backward wave signal path without interrupting the operating signal path, thereby eliminating BWO while maintaining high circuit gain. Simulation results show that the S21 parameter of the isolator is less than −20 dB from 175 GHz to 228 GHz. To verify the method’s performance, particle-in-cell (PIC) simulation was conducted based on a HOM SB SWS—a T-slot coupled-cavity (TSCC) SWS. Results indicate that this method can effectively suppress BWO and shows significant improvement in gain and output power compared to traditional methods such as sever or lossy loading. Under operating conditions of 34.4 kV and 0.35 A, the circuit achieves a maximum output power of 527 W at 216 GHz, a maximum gain of 36.39 dB at 214.4 GHz, and a bandwidth of 3 GHz where the output power exceeds 300 W. Full article

The Korean government has established a goal of ‘carbon neutrality by 2050’ to prepare for an era of energy and carbon transition. As one of the measures to achieve the goal, the Korean government is also focusing on expanding offshore wind power generation. However, throughout the power generation project, delays and interruptions frequently occur due to public conflicts that arise between key stakeholders such as local residents, developers, and the government. The existing liberal governance model used to resolve public conflicts is revealing limitations due to the power imbalance between stakeholders and the uncertainty of the responsibility structure. In response, this study proposes a meta-governance model in which the government takes on the coordinating and mediating role among stakeholders as a meta-governor. Through qualitative research methods such as categorizing cases of offshore wind conflicts that have occurred in Korea and analyzing prior studies and policy reports, the analysis demonstrates that this framework can be more effective in resolving public conflicts between stakeholders. Notably, this article integrates the government’s roles as legislator, facilitator, provider, and enforcer mentioned individually in existing meta-governance model research. And it proposes an implementation system designed to manage public conflicts and increase project acceptance in the process of promoting offshore wind power generation. This paper addresses the limitations of existing liberal governance and redefines the role of the meta-governor in meta-governance, thereby providing sustainable implementation strategies for large-scale infrastructure projects including offshore wind power development. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article